Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations3000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory211.1 KiB
Average record size in memory72.0 B

Variable types

Numeric9

Alerts

households is highly overall correlated with population and 2 other fieldsHigh correlation
latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
median_house_value is highly overall correlated with median_incomeHigh correlation
median_income is highly overall correlated with median_house_valueHigh correlation
population is highly overall correlated with households and 2 other fieldsHigh correlation
total_bedrooms is highly overall correlated with households and 2 other fieldsHigh correlation
total_rooms is highly overall correlated with households and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-03-28 18:06:20.820708
Analysis finished2025-03-28 18:06:32.216476
Duration11.4 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

High correlation 

Distinct607
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.5892
Minimum-124.18
Maximum-114.49
Zeros0
Zeros (%)0.0%
Negative3000
Negative (%)100.0%
Memory size23.6 KiB
2025-03-28T18:06:32.332750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-124.18
5-th percentile-122.47
Q1-121.81
median-118.485
Q3-118.02
95-th percentile-117.1
Maximum-114.49
Range9.69
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation1.9949363
Coefficient of variation (CV)-0.016681576
Kurtosis-1.3627717
Mean-119.5892
Median Absolute Deviation (MAD)1.275
Skewness-0.29785763
Sum-358767.6
Variance3.9797708
MonotonicityNot monotonic
2025-03-28T18:06:32.468714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.21 26
 
0.9%
-118.26 26
 
0.9%
-118.28 25
 
0.8%
-118.27 25
 
0.8%
-118.29 25
 
0.8%
-118.3 24
 
0.8%
-118.14 23
 
0.8%
-118.35 22
 
0.7%
-118.33 21
 
0.7%
-118.31 21
 
0.7%
Other values (597) 2762
92.1%
ValueCountFrequency (%)
-124.18 1
 
< 0.1%
-124.17 1
 
< 0.1%
-124.16 4
0.1%
-124.15 1
 
< 0.1%
-124.14 3
0.1%
-124.1 1
 
< 0.1%
-124.09 2
0.1%
-124.01 1
 
< 0.1%
-123.92 1
 
< 0.1%
-123.85 1
 
< 0.1%
ValueCountFrequency (%)
-114.49 1
 
< 0.1%
-114.55 1
 
< 0.1%
-114.61 1
 
< 0.1%
-114.62 1
 
< 0.1%
-114.98 1
 
< 0.1%
-115.49 1
 
< 0.1%
-115.52 1
 
< 0.1%
-115.56 1
 
< 0.1%
-115.57 4
0.1%
-115.59 1
 
< 0.1%

latitude
Real number (ℝ)

High correlation 

Distinct587
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.63539
Minimum32.56
Maximum41.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-03-28T18:06:32.604256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32.56
5-th percentile32.82
Q133.93
median34.27
Q337.69
95-th percentile38.97
Maximum41.92
Range9.36
Interquartile range (IQR)3.76

Descriptive statistics

Standard deviation2.1296695
Coefficient of variation (CV)0.059762767
Kurtosis-1.1243725
Mean35.63539
Median Absolute Deviation (MAD)1.25
Skewness0.45981594
Sum106906.17
Variance4.5354923
MonotonicityNot monotonic
2025-03-28T18:06:32.738560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.02 35
 
1.2%
34.06 33
 
1.1%
34.05 32
 
1.1%
34.11 31
 
1.0%
34.09 31
 
1.0%
34.07 31
 
1.0%
33.91 30
 
1.0%
33.93 30
 
1.0%
33.84 28
 
0.9%
34.03 27
 
0.9%
Other values (577) 2692
89.7%
ValueCountFrequency (%)
32.56 1
 
< 0.1%
32.57 3
0.1%
32.58 6
0.2%
32.59 2
 
0.1%
32.6 1
 
< 0.1%
32.61 4
0.1%
32.62 2
 
0.1%
32.64 2
 
0.1%
32.66 3
0.1%
32.67 1
 
< 0.1%
ValueCountFrequency (%)
41.92 1
< 0.1%
41.8 1
< 0.1%
41.63 1
< 0.1%
41.54 1
< 0.1%
41.31 1
< 0.1%
41.28 1
< 0.1%
41.23 1
< 0.1%
41.2 1
< 0.1%
41.01 1
< 0.1%
40.99 1
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.845333
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-03-28T18:06:32.886556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q337
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.555396
Coefficient of variation (CV)0.43526609
Kurtosis-0.80378373
Mean28.845333
Median Absolute Deviation (MAD)10
Skewness0.018513121
Sum86536
Variance157.63796
MonotonicityNot monotonic
2025-03-28T18:06:33.076390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 173
 
5.8%
35 118
 
3.9%
36 115
 
3.8%
16 107
 
3.6%
34 102
 
3.4%
17 100
 
3.3%
32 91
 
3.0%
37 88
 
2.9%
26 88
 
2.9%
25 86
 
2.9%
Other values (42) 1932
64.4%
ValueCountFrequency (%)
1 2
 
0.1%
2 6
 
0.2%
3 12
 
0.4%
4 28
0.9%
5 39
1.3%
6 25
0.8%
7 20
0.7%
8 25
0.8%
9 27
0.9%
10 30
1.0%
ValueCountFrequency (%)
52 173
5.8%
51 11
 
0.4%
50 16
 
0.5%
49 21
 
0.7%
48 34
 
1.1%
47 22
 
0.7%
46 41
 
1.4%
45 51
 
1.7%
44 51
 
1.7%
43 56
 
1.9%

total_rooms
Real number (ℝ)

High correlation 

Distinct2215
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2599.5787
Minimum6
Maximum30450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-03-28T18:06:33.230367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile585.9
Q11401
median2106
Q33129
95-th percentile6016.45
Maximum30450
Range30444
Interquartile range (IQR)1728

Descriptive statistics

Standard deviation2155.5933
Coefficient of variation (CV)0.82920873
Kurtosis32.099941
Mean2599.5787
Median Absolute Deviation (MAD)815.5
Skewness4.1676374
Sum7798736
Variance4646582.6
MonotonicityNot monotonic
2025-03-28T18:06:33.378172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1787 5
 
0.2%
1778 5
 
0.2%
907 5
 
0.2%
1966 5
 
0.2%
1607 4
 
0.1%
2584 4
 
0.1%
1462 4
 
0.1%
1482 4
 
0.1%
1560 4
 
0.1%
1401 4
 
0.1%
Other values (2205) 2956
98.5%
ValueCountFrequency (%)
6 1
< 0.1%
16 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
21 1
< 0.1%
25 1
< 0.1%
32 2
0.1%
38 1
< 0.1%
40 1
< 0.1%
41 1
< 0.1%
ValueCountFrequency (%)
30450 1
< 0.1%
27870 1
< 0.1%
24121 1
< 0.1%
23915 1
< 0.1%
21988 1
< 0.1%
20354 1
< 0.1%
18132 1
< 0.1%
18123 1
< 0.1%
17470 1
< 0.1%
16590 1
< 0.1%

total_bedrooms
Real number (ℝ)

High correlation 

Distinct1055
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.95067
Minimum2
Maximum5419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-03-28T18:06:33.515315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile130.95
Q1291
median437
Q3636
95-th percentile1220.1
Maximum5419
Range5417
Interquartile range (IQR)345

Descriptive statistics

Standard deviation415.65437
Coefficient of variation (CV)0.78432653
Kurtosis28.537071
Mean529.95067
Median Absolute Deviation (MAD)165
Skewness3.8633932
Sum1589852
Variance172768.55
MonotonicityNot monotonic
2025-03-28T18:06:33.659371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
314 15
 
0.5%
270 12
 
0.4%
299 11
 
0.4%
459 10
 
0.3%
493 10
 
0.3%
528 10
 
0.3%
301 10
 
0.3%
348 10
 
0.3%
292 10
 
0.3%
458 10
 
0.3%
Other values (1045) 2892
96.4%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
7 2
 
0.1%
8 5
0.2%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
14 3
0.1%
ValueCountFrequency (%)
5419 1
< 0.1%
5033 1
< 0.1%
5027 1
< 0.1%
4585 1
< 0.1%
4522 1
< 0.1%
4135 1
< 0.1%
4055 1
< 0.1%
3493 1
< 0.1%
3173 1
< 0.1%
2971 1
< 0.1%

population
Real number (ℝ)

High correlation 

Distinct1802
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1402.7987
Minimum5
Maximum11935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-03-28T18:06:33.800651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile346.95
Q1780
median1155
Q31742.75
95-th percentile3238.3
Maximum11935
Range11930
Interquartile range (IQR)962.75

Descriptive statistics

Standard deviation1030.543
Coefficient of variation (CV)0.73463358
Kurtosis16.443268
Mean1402.7987
Median Absolute Deviation (MAD)450
Skewness2.9496707
Sum4208396
Variance1062018.9
MonotonicityNot monotonic
2025-03-28T18:06:33.959875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
870 7
 
0.2%
753 6
 
0.2%
697 6
 
0.2%
881 6
 
0.2%
1211 6
 
0.2%
1581 5
 
0.2%
766 5
 
0.2%
850 5
 
0.2%
568 5
 
0.2%
705 5
 
0.2%
Other values (1792) 2944
98.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
8 2
0.1%
14 2
0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
27 3
0.1%
29 1
 
< 0.1%
ValueCountFrequency (%)
11935 1
< 0.1%
11139 1
< 0.1%
10877 1
< 0.1%
9419 1
< 0.1%
8824 1
< 0.1%
8768 1
< 0.1%
8152 1
< 0.1%
7604 1
< 0.1%
7596 1
< 0.1%
7560 1
< 0.1%

households
Real number (ℝ)

High correlation 

Distinct1026
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean489.912
Minimum2
Maximum4930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-03-28T18:06:34.099461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile122.95
Q1273
median409.5
Q3597.25
95-th percentile1113
Maximum4930
Range4928
Interquartile range (IQR)324.25

Descriptive statistics

Standard deviation365.42271
Coefficient of variation (CV)0.74589459
Kurtosis26.229361
Mean489.912
Median Absolute Deviation (MAD)153.5
Skewness3.5597534
Sum1469736
Variance133533.76
MonotonicityNot monotonic
2025-03-28T18:06:34.257462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
614 12
 
0.4%
273 12
 
0.4%
375 12
 
0.4%
429 11
 
0.4%
335 11
 
0.4%
287 11
 
0.4%
456 11
 
0.4%
363 11
 
0.4%
340 11
 
0.4%
239 11
 
0.4%
Other values (1016) 2887
96.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 2
 
0.1%
7 2
 
0.1%
8 2
 
0.1%
9 5
0.2%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
14 3
0.1%
ValueCountFrequency (%)
4930 1
< 0.1%
4855 1
< 0.1%
4176 1
< 0.1%
3958 1
< 0.1%
3293 1
< 0.1%
3252 1
< 0.1%
3197 1
< 0.1%
2964 1
< 0.1%
2651 1
< 0.1%
2392 1
< 0.1%

median_income
Real number (ℝ)

High correlation 

Distinct2578
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8072718
Minimum0.4999
Maximum15.0001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-03-28T18:06:34.395501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.56239
Q12.544
median3.48715
Q34.656475
95-th percentile6.97549
Maximum15.0001
Range14.5002
Interquartile range (IQR)2.112475

Descriptive statistics

Standard deviation1.8545117
Coefficient of variation (CV)0.48709728
Kurtosis5.6261841
Mean3.8072718
Median Absolute Deviation (MAD)1.02845
Skewness1.6985117
Sum11421.815
Variance3.4392138
MonotonicityNot monotonic
2025-03-28T18:06:34.544937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0001 9
 
0.3%
4 8
 
0.3%
3.375 8
 
0.3%
3.25 7
 
0.2%
3.875 7
 
0.2%
2.125 7
 
0.2%
2.75 7
 
0.2%
3.625 6
 
0.2%
2.625 6
 
0.2%
4.5 6
 
0.2%
Other values (2568) 2929
97.6%
ValueCountFrequency (%)
0.4999 1
 
< 0.1%
0.536 3
0.1%
0.5495 1
 
< 0.1%
0.7054 1
 
< 0.1%
0.7403 1
 
< 0.1%
0.75 1
 
< 0.1%
0.8054 1
 
< 0.1%
0.8185 1
 
< 0.1%
0.8252 1
 
< 0.1%
0.844 1
 
< 0.1%
ValueCountFrequency (%)
15.0001 9
0.3%
14.2867 1
 
< 0.1%
13.6623 1
 
< 0.1%
12.8763 1
 
< 0.1%
12.6417 1
 
< 0.1%
12.3767 1
 
< 0.1%
11.806 1
 
< 0.1%
11.7794 1
 
< 0.1%
11.5706 1
 
< 0.1%
11.1978 1
 
< 0.1%

median_house_value
Real number (ℝ)

High correlation 

Distinct1784
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205846.27
Minimum22500
Maximum500001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-03-28T18:06:35.092204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22500
5-th percentile67785
Q1121200
median177650
Q3263975
95-th percentile465640
Maximum500001
Range477501
Interquartile range (IQR)142775

Descriptive statistics

Standard deviation113119.69
Coefficient of variation (CV)0.54953478
Kurtosis0.395399
Mean205846.27
Median Absolute Deviation (MAD)68000
Skewness0.98956191
Sum6.1753882 × 108
Variance1.2796064 × 1010
MonotonicityNot monotonic
2025-03-28T18:06:35.255484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001 125
 
4.2%
137500 23
 
0.8%
162500 21
 
0.7%
225000 17
 
0.6%
350000 14
 
0.5%
187500 13
 
0.4%
100000 13
 
0.4%
87500 13
 
0.4%
112500 12
 
0.4%
275000 11
 
0.4%
Other values (1774) 2738
91.3%
ValueCountFrequency (%)
22500 1
< 0.1%
37500 1
< 0.1%
39200 1
< 0.1%
39800 1
< 0.1%
40000 1
< 0.1%
41500 1
< 0.1%
42500 1
< 0.1%
42700 1
< 0.1%
43100 1
< 0.1%
43300 1
< 0.1%
ValueCountFrequency (%)
500001 125
4.2%
500000 4
 
0.1%
495800 1
 
< 0.1%
495500 1
 
< 0.1%
494700 1
 
< 0.1%
493200 1
 
< 0.1%
492300 1
 
< 0.1%
492000 1
 
< 0.1%
489800 1
 
< 0.1%
487100 1
 
< 0.1%

Interactions

2025-03-28T18:06:30.829022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:21.241934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.232754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:23.663563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:25.093910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.349938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.356574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:28.381678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:29.734832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:30.946292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:21.363866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.331792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:23.801144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:25.263483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.451934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.467334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:28.493008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:29.869032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:31.063216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:21.470106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.446240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:23.967501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:25.428123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.554634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.566681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:28.600583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:30.002990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:31.180933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:21.572244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.555376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:24.106839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:25.595028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.668543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.678538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:28.709450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:30.123264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:31.297265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:21.675858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.656317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:24.264774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:25.764786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.775043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.791142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:28.834964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:30.238071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:31.405255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:21.792007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.755539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:24.408918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:25.871711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.878354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.906834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:29.287441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:30.353652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:31.521181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:21.905347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.855577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:24.569030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.011978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.024424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:28.040215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:29.402348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:30.470770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:31.631809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.016589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:23.348765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:24.712674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.123800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.141909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:28.158684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:29.508171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:30.581234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:31.758540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:22.124877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:23.517196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:24.892864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:26.235588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:27.252846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:28.271047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:29.625510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T18:06:30.694652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-28T18:06:35.359433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
householdshousing_median_agelatitudelongitudemedian_house_valuemedian_incomepopulationtotal_bedroomstotal_rooms
households1.000-0.275-0.0630.0450.1380.0690.9000.9730.908
housing_median_age-0.2751.0000.002-0.1170.066-0.173-0.267-0.301-0.356
latitude-0.0630.0021.000-0.880-0.156-0.080-0.120-0.047-0.008
longitude0.045-0.117-0.8801.000-0.067-0.0080.1140.0520.031
median_house_value0.1380.066-0.156-0.0671.0000.6590.0320.1150.222
median_income0.069-0.173-0.080-0.0080.6591.0000.0490.0350.300
population0.900-0.267-0.1200.1140.0320.0491.0000.8640.813
total_bedrooms0.973-0.301-0.0470.0520.1150.0350.8641.0000.920
total_rooms0.908-0.356-0.0080.0310.2220.3000.8130.9201.000

Missing values

2025-03-28T18:06:31.936286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-28T18:06:32.077087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
0-122.0537.3727.03885.0661.01537.0606.06.6085344700.0
1-118.3034.2643.01510.0310.0809.0277.03.5990176500.0
2-117.8133.7827.03589.0507.01484.0495.05.7934270500.0
3-118.3633.8228.067.015.049.011.06.1359330000.0
4-119.6736.3319.01241.0244.0850.0237.02.937581700.0
5-119.5636.5137.01018.0213.0663.0204.01.663567000.0
6-121.4338.6343.01009.0225.0604.0218.01.664167000.0
7-120.6535.4819.02310.0471.01341.0441.03.2250166900.0
8-122.8438.4015.03080.0617.01446.0599.03.6696194400.0
9-118.0234.0831.02402.0632.02830.0603.02.3333164200.0
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
2990-118.2334.0949.01638.0456.01500.0430.02.6923150000.0
2991-117.1734.2813.04867.0718.0780.0250.07.1997253800.0
2992-122.3337.3952.0573.0102.0232.092.06.2263500001.0
2993-117.9133.6037.02088.0510.0673.0390.05.1048500001.0
2994-117.9333.8635.0931.0181.0516.0174.05.5867182500.0
2995-119.8634.4223.01450.0642.01258.0607.01.1790225000.0
2996-118.1434.0627.05257.01082.03496.01036.03.3906237200.0
2997-119.7036.3010.0956.0201.0693.0220.02.289562000.0
2998-117.1234.1040.096.014.046.014.03.2708162500.0
2999-119.6334.4242.01765.0263.0753.0260.08.5608500001.0